Introduction to Econometrics 4e

en Limba Engleză Paperback – 15 oct 2009

The word "econometrics" literally means "measurement in economics." A revision of the landmark text, Introduction to Econometrics offers, in its new fourth edition, a fresh accessible, and well-written introduction to the subject. With a rigorous pedagogical framework, which sets it apart from comparable texts, the book also includes such Web-based supplements as an instructor's manual and data sets.

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Specificații

ISBN-13: 9780470015124
ISBN-10: 0470015128
Pagini: 654
Ilustrații: Illustrations
Dimensiuni: 189 x 235 x 35 mm
Greutate: 1.18 kg
Ediția:4th Edition
Editura: Wiley
Locul publicării:Chichester, United Kingdom

Public țintă

Students taking an introductory course in econometrics

Notă biografică

G.S.Maddala was one of the leading figures in field of econometrics for more than 30 years until he passed away in 1999. At the time of his death, he held the University Eminent Scholar Professorship in the Department of Economics at Ohio State University. His previous affiliations include Stanford University, University of Rochester and University of Florida. Kajal Lahiri is Distinguished Professor of Economics, and Health Policy, and Management and Behaviour at the State University of New York, Albany where he is also Director of the Econometric Research Institute. Professor Lahiri is an Honorary Fellow of the International Institute of Forecasters.

Cuprins

Part I: Introduction and the Linear Regression Model Chapter 1 What is Econometrics? Summary and an Outline of the Book References Chapter 2 Statistical Background and Matrix Algebra Addition Rules of Probability Conditional Probability and the Multiplication Rule Bayes' Theorem Summation and Product Operations Joint, Marginal, and Conditional Distributions Illustrative Example The Normal Distribution Related Distributions Point Estimation Unbiasedness Efficiency Consistency Other Asymptotic Properties Summary Exercises Appendix: Matrix Algebra Exercises on Matrix Algebra References Chapter 3 Simple Regression Example 1: Simple Regression Example 2: Multiple Regression Illustrative Example Reverse Regression Illustrative Example Illustrative Example Confidence Intervals for a, b, and s² Testing of Hypotheses Example of Comparing Test Scores from the GRE and GMAT Tests Regression with No Constant Term Prediction of Expected Values Illustrative Example Some Illustrative Examples Illustrative Example The Bivariate Normal Distribution Galton's Result and the Regression Fallacy A Note on the Term "Regression" Summary Exercises Appendix: Proofs References Chapter 4 Multiple Regression The Least Squares Method Illustrative Example Illustrative Example Formulas for the General Case of k Explanatory Variables Some Illustrative Examples Illustrative Example Two Illustrative Examples Illustrative Example Nested and Nonnested Hypotheses Tests for Linear Functions of Parameters Illustrative Example Omission of Relevant Variables Example 1: Demand for Food in the United States Example 2: Production Functions and Management Bias Inclusion of Irrelevant Variables The Analysis of Variance Test Example 1: Stability of the Demand for Food Function Example 2: Stability of Production Functions Predictive Tests for Stability Illustrative Example Illustrative Example Summary Exercises Appendix 4.1: The Multiple Regression Model in Matrix Notation Appendix 4.2: Nonlinear Regressions Appendix 4.3: Large-Sample Theory Data Sets References Part II: Violation of the Assumptions of the Basic Model Chapter 5 Heteroskedasticity Illustrative Example Illustrative Example Some Other Tests Illustrative Example An Intuitive Justification for the Breusch-Pagan Test Estimation of the Variance of the OLS Estimator under Heteroskedasticity Illustrative Example Illustrative Example: The Density Gradient Model The Box-Cox Test The BM Test The PE Test Summary Exercises Appendix: Generalized Least Squares References Chapter 6 Autocorrelation Illustrative Example Some Illustrative Examples Iterative Procedures Grid-Search Procedures The von Neumann Ratio The Berenblut-Webb Test Durbin's h-Test Durbin's Alternative Test Illustrative Example Errors Not AR(1) Autocorrelation Caused by Omitted Variables Serial Correlation Due to Misspecified Dynamics The Wald Test Illustrative Example Spurious Trends Differencing and Long-Run Effects: The Concept of Cointegration Summary Exercises References Chapter 7 Multicollinearity Using Ratios or First Differences Using Extraneous Estimates Getting More Data Summary Exercises Appendix: Linearly Dependent Explanatory Variables References Chapter 8 Dummy Variables and Truncated Variables Illustrative Example Two More Illustrative Examples The Linear Probability Model The Linear Discriminant Function Illustrative Example The Problem of Disproportionate Sampling Prediction of Effects of Changes in the Explanatory Variables Measuring Goodness of Fit Some Examples Method of Estimation Limitations of the Tobit Model The Truncated Regression Model Summary Exercises References Chapter 9 Simultaneous Equations Models Illustrative Example Illustrative Example Measuring R² Illustrative Example Computing Standard Errors Illustrative Example Illustrative Example Working's Concept of Identification Recursive Systems Estimation of Cobb-Douglas Production Functions Weak Exogeneity Superexogeneity Strong Exogeneity Granger Causality Granger Causality and Exogeneity Tests for Exogeneity Summary Exercises Appendix References Chapter 10 Diagnostic Checking, Model Selection, and Specification Testing Tests for Omitted Variables Tests for ARCH Effects Predicted Residuals and Studentized Residuals Dummy Variable Method for Studentized Residuals BLUS Residuals Recursive Residuals Illustrative Example Illustrative Example Hypothesis-Testing Search Interpretive Search Simplification Search Proxy Variable Search Data Selection Search Post-Data Model Construction Hendry's Approach to Model Selection Theil's Criterion Criteria Based on Minimizing the Mean-Squared Error of Prediction Akaike's Information Criterion Bayes' Theorem and Posterior Odds for Model Selection An Application: Testing for Errors in Variables or Exogeneity Some Illustrative Examples An Omitted Variable Interpretation of the Hausman Test The Davidson and MacKinnon Test The Encompassing Test A Basic Problem in Testing Nonnested Hypotheses Hypothesis Testing versus Model Selection as a Research Strategy Tests for Normality Summary Exercises Appendix References Chapter 11 Errors in Variables Two Explanatory Variables: One Measured with Error Illustrative Example Two Explanatory Variables: Both Measured with Error Coefficient of the Proxy Variable The Case of Multiple Equations Correlated Errors Summary Exercises References Part III: Special Topics Chapter 12 Introduction to Time-Series Analysis Strict Stationarity Weak Stationarity Properties of Autocorrelation Function Nonstationarity Purely Random Process Random Walk Moving Average Process Autoregressive Process Autoregressive Moving Average Process Autoregressive Integrated Moving Average Process Estimation of MA Models Estimation of ARMA Models Residuals from the ARMA Models Testing Goodness of Fit Forecasting from Box-Jenkins Models Illustrative Example Trend Elimination: The Traditional Method A Summary Assessment Seasonality in the Box-Jenkins Modeling Summary Exercises Data Sets References Chapter 13 Models of Expectations and Distributed Lags Estimation in the Autoregressive Form Estimation in the Distributed Lag Form Finite Lags: The Polynomial Lag Illustrative Example Choosing the Degree of the Polynomial Case 1 Case 2 Illustrative Example Summary Exercises References Chapter 14 Vector Autoregressions, Unit Roots, and Cointegration The Dickey-Fuller Tests The Serial Correlation Problem The Low Power of Unit Root Tests The DF-GLS Test What are the Null and Alternative Hypotheses in Unit Root Tests? Tests with Stationarity as Null Confirmatory Analysis Panel Data Unit Root Tests Structural Change and Unit Roots Summary Exercises References Chapter 15 Panel Data Analysis Illustrative Example: Fixed Effect Estimation Hausman Test Breusch and Pagan Test Tests for Serial Correlation Summary References Chapter 16 Small-Sample Inference: Resampling Methods More Efficient Monte Carlo Methods Response Surfaces Some Illustrative Examples Other Issues Relating to the Bootstrap Heteroskedasticity and Autocorrelation Unit Root Tests Based on the Bootstrap Cointegration Tests Summary References Appendix Index

Descriere

Now in its fourth edition, this landmark text provides a fresh, accessible and well-written introduction to the subject. With a rigorous pedagogical framework, which sets it apart from comparable texts, the latest edition features an expanded website providing numerous real life data sets and examples.

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